Deep learning methods are well known for their state-of-the-art performances on a variety of tasks [1]}, [2]}, [3]}. However, they often require to be trained on large labeled datasets to acquire robust and generalizable features. Few-Shot Learning (FSL) [4]}, [5]}, [6]} aims at reducing this burden by defining a distribution over tasks, with each task containing a few labeled data points (support set) and a set of target data (query set) belonging to the same set of classes. A common way to train FSL methods is through episodic meta-training [7]} with the model repeatedly exposed to batches of tasks sampled from a task-distribution and then tested on a different but similar distribution in the meta-testing phase. The prefix “meta” is commonly used to distinguish the high-level training and evaluation routines of meta-learning (outer loop), from the training and evaluation routines at the single-task level (inner loop).
